Can Qwen3-Coder-Next run on NVIDIA A800 80GB?

YES — Runs Great

S97Excellent

Estimated from fit model

Qwen3-Coder-Next needs ~59.5 GB VRAM. NVIDIA A800 80GB has 80.0 GB. With Q4_K_M quantization, expect ~102 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 59.5 GB, 101.9 tok/s, Runs well

59.5 GB required80.0 GB available

74% VRAM used

Fit status

Runs well

Decode

101.9 tok/s

TTFT

1899 ms

Safe context

240K

Memory

59.5 GB / 80.0 GB

Memory breakdown

Weights48.8 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsQwen3-Coder-Next on NVIDIA A800 80GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 101.9 tok/s decode · 1.9s TTFT (warm) · 255 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	101.9 tok/s	1036 ms	240K
Coding	S	Runs well	101.9 tok/s	1899 ms	240K
Agentic Coding	S	Runs well	101.9 tok/s	2763 ms	240K
Reasoning	S	Runs well	101.9 tok/s	2245 ms	240K
RAG	S	Runs well	101.9 tok/s	3454 ms	240K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on NVIDIA A800 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	A85
Q3_K_S	3	39.2 GB	Low	S87
NVFP4	4	44.8 GB	Medium	S88
Q4_K_M	4	48.8 GB	Medium	S88
Q5_K_M	5	57.6 GB	High	S88
Q6_KBest for your GPU	6	65.6 GB	High	S88
Q8_0	8	85.6 GB	Very High	F0
F16	16	164.0 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen3-Coder-Next on your machine.

Run

ollama run qwen3-coder-next

Your hardware

More models your NVIDIA A800 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	15.5 tok/s
Qwen 3.5 122B A10B	122B	A	45.9 tok/s
Mistral Small 4 119B	119B	A	48.7 tok/s
GPT-OSS 120B	117B	A	17.6 tok/s
Command A 111B	111B	S	20.4 tok/s

Frequently asked questions

Can NVIDIA A800 80GB run Qwen3-Coder-Next?

Yes, NVIDIA A800 80GB can run Qwen3-Coder-Next with a S grade (Runs well). Expected decode speed: 101.9 tok/s.

How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 59.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-Coder-Next?

The recommended quantization for Qwen3-Coder-Next is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder-Next run at on NVIDIA A800 80GB?

On NVIDIA A800 80GB, Qwen3-Coder-Next achieves approximately 101.9 tokens per second decode speed with a time-to-first-token of 1899ms using Q4_K_M quantization.

Can NVIDIA A800 80GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on NVIDIA A800 80GB receives a S grade with 101.9 tok/s and 240K context.

What context window can Qwen3-Coder-Next use on NVIDIA A800 80GB?

On NVIDIA A800 80GB, Qwen3-Coder-Next can safely use up to 240K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA A800 80GB See all hardware for Qwen3-Coder-Next

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/qwen-3-coder-next-on-a800-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

101.9 tok/s

1036 ms

240K

Coding

Runs well

101.9 tok/s

1899 ms

240K

Agentic Coding

Runs well

101.9 tok/s

2763 ms

240K

Reasoning

Runs well

101.9 tok/s

2245 ms

240K

RAG

Runs well

101.9 tok/s

3454 ms

240K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on NVIDIA A800 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	A85
Q3_K_S	3	39.2 GB	Low	S87
NVFP4	4	44.8 GB	Medium	S88
Q4_K_M	4	48.8 GB	Medium	S88
Q5_K_M	5	57.6 GB	High	S88
Q6_KBest for your GPU	6	65.6 GB	High	S88
Q8_0	8	85.6 GB	Very High	F0
F16	16	164.0 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

15.5 tok/s

Qwen 3.5 122B A10B

122B

45.9 tok/s

Mistral Small 4 119B

119B

48.7 tok/s

GPT-OSS 120B

117B

17.6 tok/s

Command A 111B

111B

20.4 tok/s

Frequently asked questions

Can NVIDIA A800 80GB run Qwen3-Coder-Next?

Yes, NVIDIA A800 80GB can run Qwen3-Coder-Next with a S grade (Runs well). Expected decode speed: 101.9 tok/s.

How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 59.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-Coder-Next?

The recommended quantization for Qwen3-Coder-Next is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder-Next run at on NVIDIA A800 80GB?

On NVIDIA A800 80GB, Qwen3-Coder-Next achieves approximately 101.9 tokens per second decode speed with a time-to-first-token of 1899ms using Q4_K_M quantization.

Can NVIDIA A800 80GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on NVIDIA A800 80GB receives a S grade with 101.9 tok/s and 240K context.

What context window can Qwen3-Coder-Next use on NVIDIA A800 80GB?

On NVIDIA A800 80GB, Qwen3-Coder-Next can safely use up to 240K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.